The current practice for monitoring of subsurface plumes involves the collection of water samples from sparsely distributed monitoring wells and laboratory analysis to determine chemical concentrations. In most field situations, cost and time constraints limit the number of samples that could be collected and analyzed for continuous monitoring of large, transient plumes. With the development of wireless sensor networks ͑WSNs͒, that allow sensors to be incorporated into a distributed wireless communication and processing system, the potential exists to develop new, efficient, economical, large-scale subsurface data collection and monitoring methods. This paper presents a proof-of-concept study conducted in a two-dimensional synthetic aquifer constructed in an intermediate scale test tank to demonstrate the feasibility of using WSN for subsurface plume monitoring. The tank was packed to represent a heterogeneous aquifer, and a sodium bromide tracer was used to create a plume. A set of ten wireless sensor nodes ͑motes͒ equipped with conductivity probes to measure electrical conductivity formed the network. Software for automated data acquisition was developed and tested. Results of two experiments conducted using this test system are presented. The lessons learned from the first experiment were used to make modifications to the way the sensors were placed, how they were calibrated and how the sensors were interfaced with the data acquisition system. The findings are used to identify future research directions and issues that need to be addressed before field implementations of a WSN based data collection system for plume monitoring.

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We consider the convergecast problem in wireless sensor networks where readings generated by each sensor node are to reach the sink. Since a sensor reading can usually be encoded in a few bytes, more than one reading can readily fit into a standard transmission packet. We assume that any such packet consumes one unit of energy every time it hops from a node to a neighbor regardless of the total size of the readings in it. Our objective is to minimize the total energy consumed to send all the readings to the sink. Consequently, we ask the question: can we pack the readings in common routes to minimize the number of hops? It is quite elementary to see that this problem is NP-hard when the size of the readings are arbitrary via reductions from bin packing or set partition.We study the simple version with readings normalized to 1 byte in length. However, we make no assumptions on the underlying graph. We show this to be NP-hard by way of a reduction from Set Cover. We study a class SPEP of distributed algorithms that is completely defined by two properties. Firstly, the packets hop along some shortest path to the sink. Secondly, given all the readings that enter into a node, it sends out as many fully packed packets as possible followed by at most one partial packet -the elementary packing property. We show that any algorithm in this class is (2− 3 2k )-approximate where k ≥ 2 is the size of a data packet in bytes. We additionally show that this class is optimal when the underlying sensor network is a tree or grid topology. Our main technical contribution is a lower bound. We show that no algorithm that either follows the shortest path or packs in an elementary manner is a (2 − )-approximation, for any fixed > 0.

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